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An integrated dataset of energy efficiency measures published as linked open data


Despite an extensive energy efficiency potential, measures are sometimes not adopted due to barriers, such as lack of information. An integrated database of available energy efficiency measures, which has not existed previously, is one step towards overcoming such barriers. To address this, we present a dataset (i.e., data-base) integrating energy efficiency data from Sweden (from the Swedish Energy Agency) and the USA (from the Department of Energy’s Industrial Assessment Centers), and publishing the data on the Web, using standardized Web languages and following the principles and best practices of so-called linked data. Additionally, several demonstration interfaces to access the data are provided, in order to show the potential of the result. These are entirely novel results, since this is the first dataset we are aware of that publishes this type of data using linked data principles and standards, thus integrating data from entirely different sources making them jointly searchable and reusable. Our results show that such data integration is possible, and that the integrated dataset has several benefits for different categories of users, e.g., supporting industry and energy efficiency auditors in overcoming the information barrier for investment in energy efficiency measures, and supporting application developers to more easily integrate such data into support tools for energy efficiency assessment.

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  1. Throughout this paper, we will use the term “dataset” to refer to such a database. The reason for avoiding the term “database” is that it has historically been used to refer primarily to a specific database technology, i.e., relational databases, while in this paper, we deal with another kind of database, i.e., a graph database, which is commonly denoted a “dataset” when published on the Web.

  2. The technical principles behind linked data were presented by Berners-Lee (2009). For an updated view of the current linked open data cloud, see illustrations at or the list of datasets tagged with LOD available from








  10. In our data sources, pay-off time and payback time are both used, hence in this paper, we use them as synonyms.

  11. LOD applies the principles of LD, while additionally making the data open, i.e., freely available on the Web. In this section, we discuss the underlying principles of LD, which apply even if the data is not openly available.

  12. Note that this applies to the technology to publish data—we still cannot necessarily guarantee that we “speak the same language” when it comes to what we mean by different concepts and data elements. The latter is of course also important, but is solve at a later stage, by publishing the data model, i.e., ontology, also using a standardized language.

  13. The current version of the vocabulary is available here (in OWL-format):

  14. We are already using relations from the SKOS vocabulary for other purposes in this project (see Table 3), hence, this would be a natural step to take in future work.

  15. Tool can be downloaded here:

  16. In our case we used the open source Sesame RDF store that can be found here:

  17. Available here: We have reused and modified the open source SNORQL interface available here:

  18. English version available here:

  19. Current release available here:

  20. We would like to point out that these performance issues are not due to the linked data technologies or systems used, but are simply an effect of this being a demonstration application where performance was not in focus, running on servers in the university’s research environment, intended only for demonstration projects, not deployed services although open to the public.

  21. Note that this does not mean the dataset residing on our servers would only contain the latest additions to the data, it will always contain the complete set of data, incrementally built through additions of the latest data as it is loaded into the dataset. However, when a query is sent, the latest data, in addition to older data, is always available for retrieval, which may not be the case for a local copy that was downloaded some time ago.


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This research was carried out within the DEFRAM project. We kindly thank the Swedish Energy Agency for financial support of DEFRAM, and we would in particular like to thank the project officer, Lara Kruse, for all the support given to this project. We would also like to express our thanks to Prof. Michael R. Muller, Rutgers University, who kindly allowed us to reuse the IAC database within our project. We additionally thank Robin Keskisärkkä for developing the demonstration interface and providing the screenshots used in this paper for illustrating it. Finally, we give acknowledgement to Svetlana Paramonova, Danica Ilic, Kaihong Sun, Joel Forsberg, and Joel sterqvist for the processing and quality controlling of the EKC-dataset.

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Correspondence to Eva Blomqvist.

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This research was conducted within the DEFRAM project funded by the Swedish Energy Agency; for more information, see the DEFRAM website:

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Blomqvist, E., Thollander, P. An integrated dataset of energy efficiency measures published as linked open data. Energy Efficiency 8, 1125–1147 (2015).

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  • Energy efficiency audit
  • Energy efficiency improvement
  • Energy efficiency data
  • Linked data